Myocardial ischemia detection using Hidden Markov principal component analysis

Journal Article

This paper introduces a new temporal version of Principal Component Analysis by using a Hidden Markov Model in order to obtain optimized representations of observed data through time. The novelty of the proposed method consists mainly in the way in which a static dimensionality reduction technique has been combined with a classic mixture model in time, to enhance the capabilities of dimensionality reduction and classification of myocardial ischemia data. Experimental results show improvements in classification accuracies even with highly reduced representations. © Springer-Verlag Berlin Heidelberg 2007.

Full Text

Duke Authors

Cited Authors

  • Alvarez López, MA; Henao, R; Orozco, A

Published Date

  • January 1, 2008

Published In

Volume / Issue

  • 18 /

Start / End Page

  • 99 - 103

International Standard Serial Number (ISSN)

  • 1680-0737

Digital Object Identifier (DOI)

  • 10.1007/978-3-540-74471-9_24

Citation Source

  • Scopus